Let's now talk about the development environment you will use throughout this specialization. We're going to be talking about Python notebooks in the Cloud. We will learn how to develop Machine Learning Models in Python notebooks, where the notebook servers on the Cloud. We'll also learn how to execute ad-hoc queries using serverless technologies and do this from those Python notebooks. Not every Machine Learning Model needs to be built from scratch. Also in this module, my colleague Sarah, will show you how to invoke pre-trained Machine Learning Models. Cloud Datalab is basically the integrated development environment you'll be using to write your code in this course. Cloud Datalab notebooks run on virtual machines and because of that we will talk about Compute Engine and Cloud Storage, Why? Two things follow from the fact that Cloud Datalab runs on a VM. First, it means that you can actually control and change what sort of machine is running your notebook by for example, giving it more memory or adding a GPU without having to rewrite your notebook from scratch. Re-hosting a notebook on a more powerful machine is trivially easy. Second, virtual machines are ephemeral. Consequently, anything that you want to persist, anything that you want to save, you must store outside of the VM. The best place to do that, especially for large binary files, is in Cloud Storage. After reviewing how Compute Engine works, we'll review the basics of Cloud Storage. The notebooks themselves, we will store in a Cloud repository so that they're under revision control. Finally, we'll do a hands-on lab so that you can get hands-on with Datalab. We'll show you how to use Cloud Datalab together with BigQuery, which is a managed data analysis service on the Cloud, that will allow you to execute ad- hoc queries at scales and speeds that are not possible with traditional database systems. Then we will look at how to invoke pre-trained ML models and do this from within Cloud Datalab.